This foundational Artificial Intelligence (AI) course offers an in-depth exploration of the fundamental concepts, applications, and techniques in AI. It covers various branches of AI, including machine learning (ML), deep learning (DL), and computer vision (CV). The course takes students through the history and evolution of AI, types of AI systems, and key algorithms such as supervised and unsupervised learning. Topics also include Artificial Neural Networks (ANN), Deep Layer Neural Networks (DNN), and Convolutional Neural Networks (CNN). The course concludes by discussing the future of AI, its societal impacts, and ethical concerns.
Course Features:
- Comprehensive exploration of AI and its subfields, including ML, DL, and CV.
- Learn and implement key AI algorithms: supervised, unsupervised learning, ANN, DNN, and CNN.
- Practical experience in image classification and computer vision tasks.
- Focus on understanding deep learning architectures like feedforward and convolutional neural networks.
- Explore future trends in AI and its ethical and societal implications.
Prerequisites:
- Basic knowledge of Python programming is helpful, but not mandatory for this course.
Key Learning Outcomes:
By the end of this course, participants will be able to:
- Define AI and understand its history, types, and systems.
- Apply machine learning algorithms, including supervised, unsupervised, and deep learning techniques.
- Preprocess and represent data for ML and DL tasks.
- Work with image data in computer vision tasks like image classification.
- Use deep learning techniques (ANN, DNN, CNN) for complex computer vision tasks.
- Analyze AI’s future trends and understand its impact on society, including ethical and safety considerations.
- Combine theoretical knowledge and practical skills to form a solid understanding of AI.
Target Audience:
- Individuals looking to pursue a career in AI, ML, DL, Data Science, or Data Analytics.
- Anyone interested in understanding the current and future landscape of AI and its real-world applications.
Test & Evaluation:
- Participants must complete all assignments for effective learning.
- A final assessment will be conducted at the end of the program to evaluate participants’ progress.
Certification:
- Successful participants will receive a Certificate of Completion.
- A Project Letter will be awarded upon the successful completion of the project.
- Students who leave the course midway or do not complete it will not receive any certification.
Delivery Mode & Duration:
- Mode: Online Live Sessions
- Duration: 120 Hours (60 Hours of Online Live Sessions + 60 Hours of Assignments)